Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide

BioSense: An automated sensing node for organismal and environmental biology

Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.

Flower Visitation through the Lens: Exploring the Foraging Behaviour of Bombus terrestris with a Computer Vision-Based Application

To understand the processes behind pollinator declines and for the conservation of pollination services, we need to understand fundamental drivers influencing pollinator behaviour. Here, we aimed to elucidate how wild bumblebees interact with three plant species and investigated their foraging behaviour with varying flower densities. We video-recorded Bombus terrestris in 60 × 60 cm quadrats of Lotus creticus, Persicaria capitata, and Trifolium pratense in urban areas of Terceira (Azores, Portugal). For the automated bumblebee detection and counting, we created deep learning-based computer vision models with custom datasets. We achieved high model accuracy of 0.88 for Lotus and Persicaria and 0.95 for Trifolium, indicating accurate bumblebee detection. In our study, flower cover was the only factor that influenced the attractiveness of flower patches, and plant species did not have an effect. We detected a significant positive effect of flower cover on the attractiveness of flower patches for flower-visiting bumblebees. The time spent per unit of inflorescence surface area was longer on the Trifolium than those on the Lotus and Persicaria. However, our result did not indicate significant differences in the time bumblebees spent on inflorescences among the three plant species. Here, we also justify computer vision-based analysis as a reliable tool for studying pollinator behavioural ecology.

Evaluation of an autonomous acoustic surveying technique for grassland bird communities in Nebraska

Monitoring trends in wildlife communities is integral to making informed land management decisions and applying conservation strategies. Birds inhabit most niches in every environment and because of this they are widely accepted as an indicator species for environmental health. Traditionally, point counts are the common method to survey bird populations, however, passive acoustic monitoring approaches using autonomous recording units have been shown to be cost-effective alternatives to point count surveys. Advancements in automatic acoustic classification technologies, such as BirdNET, can aid in these efforts by quickly processing large volumes of acoustic recordings to identify bird species. While the utility of BirdNET has been demonstrated in several applications, there is little understanding of its effectiveness in surveying declining grassland birds. We conducted a study to evaluate the performance of BirdNET to survey grassland bird communities in Nebraska by comparing this automated approach to point count surveys. We deployed ten autonomous recording units from March through September 2022: five recorders in row-crop fields and five recorders in perennial grassland fields. During this study period, we visited each site three times to conduct point count surveys. We compared focal grassland bird species richness between point count surveys and the autonomous recording units at two different temporal scales and at six different confidence thresholds. Total species richness (focal and non-focal) for both methods was also compared at five different confidence thresholds using species accumulation curves. The results from this study demonstrate the usefulness of BirdNET at estimating long-term grassland bird species richness at default confidence scores, however, obtaining accurate abundance estimates for uncommon bird species may require validation with traditional methods.

A comparison of the sampling effectiveness of acoustic recorder, camera trap and point count methods in sampling nocturnal birds in Afrotropical landscapes

Conservation decisions for bird diversity in the Afrotropics are often based on ecological studies utilizing diurnal bird species likely owing to difficulties associated with sampling nocturnal birds. It is therefore important to compare the sampling effectiveness of some of the available techniques that can be used in nocturnal bird surveys to guide future long-term survey efforts. Thus, we compared the sampling effectiveness of point count, acoustic recorder and camera trap for estimating nocturnal bird species richness and also across habitat types. We surveyed 20 points that were spaced at least 500 m apart in November and December 2021 in the Amurum Forest Reserve and its surroundings in Jos-Nigeria. At each point, we used two camera traps, one at the ground and the other at 2.0 m. We also used one acoustic recorder as well as a 15-min point count during each survey at each point. We encountered 11 nocturnal bird species, primarily nightjars but also owls. While we did not encounter any species with the camera traps, all 11 species were recorded using the acoustic recorder. All species except for Ketupa lacteaus were recorded in point count. Eight species were recorded in the gallery, seven in rocky and nine in savannah. Species richness and estimation using the acoustic recorder and point count were similar across habitat types. We conclude that either point count or acoustic recorders are useful for nocturnal bird surveys in Afrotropical environments. However, the choice of methods should be based on the research questions as some questions may be better answered by a specific method or even a combination of both.

Balancing economic and ecological functions in smallholder and industrial oil palm plantations

The expansion of the oil palm industry in Indonesia has improved livelihoods in rural communities, but comes at the cost of biodiversity and ecosystem degradation. Here, we investigated ways to balance ecological and economic outcomes of oil palm cultivation. We compared a wide range of production systems, including smallholder plantations, industrialized company estates, estates with improved agronomic management, and estates with native tree enrichment. Across all management types, we assessed multiple indicators of biodiversity, ecosystem functions, management, and landscape structure to identify factors that facilitate economic-ecological win-wins, using palm yields as measure of economic performance. Although, we found that yields in industrialized estates were, on average, twice as high as those in smallholder plantations, ecological indicators displayed substantial variability across systems, regardless of yield variations, highlighting potential for economic-ecological win-wins. Reducing management intensity (e.g., mechanical weeding instead of herbicide application) did not lower yields but improved ecological outcomes at moderate costs, making it a potential measure for balancing economic and ecological demands. Additionally, maintaining forest cover in the landscape generally enhanced local biodiversity and ecosystem functioning within plantations. Enriching plantations with native trees is also a promising strategy to increase ecological value without reducing productivity. Overall, we recommend closing yield gaps in smallholder cultivation through careful intensification, whereas conventional plantations could reduce management intensity without sacrificing yield. Our study highlights various pathways to reconcile the economics and ecology of palm oil production and identifies management practices for a more sustainable future of oil palm cultivation.

An integrated passive acoustic monitoring and deep learning pipeline for black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar

The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine mammals, birds, and bats, its application to primates is limited. Black-and-white ruffed lemurs (Varecia variegata) are a promising species to test PAM with due to their distinctive and loud roar-shrieks. Furthermore, these lemurs are challenging to monitor via traditional methods due to their fragmented and often unpredictable distribution in Madagascar's dense eastern rainforests. Our goal in this study was to develop a machine learning pipeline for automated call detection from PAM data, compare the effectiveness of PAM versus in-person observations, and investigate diel patterns in lemur vocal behavior. We did this study at Mangevo, Ranomafana National Park by concurrently conducting focal follows and deploying autonomous recorders in May-July 2019. We used transfer learning to build a convolutional neural network (optimized for recall) that automated the detection of lemur calls (57-h runtime; recall = 0.94, F1 = 0.70). We found that PAM outperformed in-person observations, saving time, money, and labor while also providing re-analyzable data. Using PAM yielded novel insights into V. variegata diel vocal patterns; we present the first published evidence of nocturnal calling. We developed a graphic user interface and open-sourced data and code, to serve as a resource for primatologists interested in implementing PAM and machine learning. By leveraging the potential of this pipeline, we can address the urgent need for effective primate population surveys to inform conservation strategies.

Novel community data in ecology-properties and prospects

New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning.

Vibroscape analysis reveals acoustic niche overlap and plastic alteration of vibratory courtship signals in ground-dwelling wolf spiders

To expand the scope of soundscape ecology to encompass substrate-borne vibrations (i.e. vibroscapes), we analyzed the vibroscape of a deciduous forest floor using contact microphone arrays followed by automated processing of large audio datasets. We then focused on vibratory signaling of ground-dwelling Schizocosa wolf spiders to test for (i) acoustic niche partitioning and (ii) plastic behavioral responses that might reduce the risk of signal interference from substrate-borne noise and conspecific/heterospecific signaling. Two closely related species - S. stridulans and S. uetzi - showed high acoustic niche overlap across space, time, and dominant frequency. Both species show plastic behavioral responses - S. uetzi males shorten their courtship in higher abundance of substrate-borne noise, S. stridulans males increased the duration of their vibratory courtship signals in a higher abundance of conspecific signals, and S. stridulans males decreased vibratory signal complexity in a higher abundance of S. uetzi signals.

Rice Fields as Important Habitats for Three Anuran Species-Significance and Implications for Conservation

Agriculture can have different effects on wildlife depending on land-use type and extensive/intensive practices. The aim of this study was to establish the significance of rice fields in Central Bulgaria as anuran habitats. We used Audiomoth acoustic loggers to record calling activity at three sites near the city of Plovdiv-one shallow pond and two rice fields-from June to September in 2022 and 2023. We registered the three most widespread species in the region-Bufotes viridis, Hyla orientalis and Pelophylax ridibundus-and created pattern-matching models for them using the free web interface Arbimon, which enabled us to perform presence/absence counts and abundance estimates. At the beginning and the end of the rice vegetation period, water samples were collected and analysed for 62 substances. Substance concentrations were compared between ponds and with LC50 data from the available literature. We registered 19 and 21 substances in 2022 and 2023, respectively, with concentrations within the accepted limits, and although some metals were near risk levels, this was not reflected in the presence counts or the abundance estimates. The results indicated that frog activity was not related to any of the registered substances, but that it was positively correlated with daily rainfall and was higher in the rice fields.

COVID-19 shutdown revealed higher acoustic diversity and vocal activity of flagship birds in old-growth than in production forests

The COVID-19 shutdown has caused a quasi-experimental situation for ecologists in Spring 2020, providing an unprecedented release in acoustic space for avian soundscapes due to the lowest technophony levels experienced for decades. We conducted large-scale passive acoustic monitoring in 68 forest stands during and after the shutdown to compare their acoustic diversity under different management regimes. We designed a before-after sampling scheme of 18 paired stands to evaluate the short-term effect of shutdown on diel and nocturnal acoustic diversity of forest soundscapes. We assessed whether old-growth preserves hosted higher acoustic diversity and vocal activity of flagship specialist birds than production stands during the shutdown, and whether the effect of management was mediated by landscape fragmentation and distance to roads. We derived acoustic richness and vocal activity of flagship specialist birds by systematically performing 15-min long aural listening to identify species vocalizations from all recorded stands. The end of the COVID-19 shutdown led to a rapid decrease in diel and nocturnal biophony and acoustic diversity. During the shutdown, we found significantly higher biophony and acoustic diversity in old-growth preserves than in production stands. Bird acoustic richness and vocalizations of the two most frequent flagship specialists, Dendrocoptes medius and Phylloscopus sibilatrix, were also both higher in old-growth stands. Interestingly, this positive effect of old-growth stands on forest soundscapes suggested that they could potentially attenuate traffic noise, because the distance to roads decreased acoustic diversity and biophony only outside old-growth preserves. Similarly, flagship bird richness increased with old-growth cover in the surrounding landscape while edge density had a negative effect on both acoustic diversity and flagship birds. We suggest that enhancing the old-growth preserve network implemented across French public forests would provide a connected frame of acoustic sanctuaries mitigating the ever-increasing effect of technophony on the acoustic diversity of temperate forest soundscapes.

VGGish-based detection of biological sound components and their spatio-temporal variations in a subtropical forest in eastern China

Passive acoustic monitoring technology is widely used to monitor the diversity of vocal animals, but the question of how to quickly extract effective sound patterns remains a challenge due to the difficulty of distinguishing biological sounds within multiple sound sources in a soundscape. In this study, we address the potential application of the VGGish model, pre-trained on Google's AudioSet dataset, for the extraction of acoustic features, together with an unsupervised clustering method based on the Gaussian mixture model, to identify various sound sources from a soundscape of a subtropical forest in China. The results show that different biotic and abiotic components can be distinguished from various confounding sound sources. Birds and insects were the two primary biophony sound sources, and their sounds displayed distinct temporal patterns across both diurnal and monthly time frames and distinct spatial patterns in the landscape. Using the clustering and modeling method of the general sound feature set, we quickly depicted the soundscape in a subtropical forest ecosystem, which could be used to track dynamic changes in the acoustic environment and provide help for biodiversity and ecological environment monitoring.

Bird Object Detection: Dataset Construction, Model Performance Evaluation, and Model Lightweighting

The application of object detection technology has a positive auxiliary role in advancing the intelligence of bird recognition and enhancing the convenience of bird field surveys. However, challenges arise due to the absence of dedicated bird datasets and evaluation benchmarks. To address this, we have not only constructed the largest known bird object detection dataset, but also compared the performances of eight mainstream detection models on bird object detection tasks and proposed feasible approaches for model lightweighting in bird object detection. Our constructed bird detection dataset of GBDD1433-2023, includes 1433 globally common bird species and 148,000 manually annotated bird images. Based on this dataset, two-stage detection models like Faster R-CNN and Cascade R-CNN demonstrated superior performances, achieving a Mean Average Precision (mAP) of 73.7% compared to one-stage models. In addition, compared to one-stage object detection models, two-stage object detection models have a stronger robustness to variations in foreground image scaling and background interference in bird images. On bird counting tasks, the accuracy ranged between 60.8% to 77.2% for up to five birds in an image, but this decreased sharply beyond that count, suggesting limitations of object detection models in multi-bird counting tasks. Finally, we proposed an adaptive localization distillation method for one-stage lightweight object detection models that are suitable for offline deployment, which improved the performance of the relevant models. Overall, our work furnishes an enriched dataset and practice guidelines for selecting suitable bird detection models.

An acoustic detection dataset of birds (Aves) in montane forests using a deep learning approach

Background

Long-term monitoring is needed to understand the statuses and trends of wildlife communities in montane forests, such as those in Yushan National Park (YSNP), Taiwan. Integrating passive acoustic monitoring (PAM) with an automated sound identifier, a long-term biodiversity monitoring project containing six PAM stations, was launched in YSNP in January 2020 and is currently ongoing. SILIC, an automated wildlife sound identification model, was used to extract sounds and species information from the recordings collected. Animal vocal activity can reflect their breeding status, behaviour, population, movement and distribution, which may be affected by factors, such as habitat loss, climate change and human activity. This massive amount of wildlife vocalisation dataset can provide essential information for the National Park's headquarters on resource management and decision-making. It can also be valuable for those studying the effects of climate change on animal distribution and behaviour at a regional or global scale.

New information

To our best knowledge, this is the first open-access dataset with species occurrence data extracted from sounds in soundscape recordings by artificial intelligence. We obtained seven bird species for the first release, with more bird species and other taxa, such as mammals and frogs, to be updated annually. Raw recordings containing over 1.7 million one-minute recordings collected between the years 2020 and 2021 were analysed and SILIC identified 6,243,820 vocalisations of seven bird species in 439,275 recordings. The automatic detection had a precision of 0.95 and the recall ranged from 0.48 to 0.80. In terms of the balance between precision and recall, we prioritised increasing precision over recall in order to minimise false positive detections. In this dataset, we summarised the count of vocalisations detected per sound class per recording which resulted in 802,670 occurrence records. Unlike data from traditional human observation methods, the number of observations in the Darwin Core "organismQuantity" column refers to the number of vocalisations detected for a specific bird species and cannot be directly linked to the number of individuals.We expect our dataset will be able to help fill the data gaps of fine-scale avian temporal activity patterns in montane forests and contribute to studies concerning the impacts of climate change on montane forest ecosystems on regional or global scales.

Performance of autonomous recorders to detect a cryptic and endangered primate species, the black lion-tamarin (Leontopithecus chrysopygus)

Information about species distribution is important for conservation but the monitoring of populations can demand a high sampling effort with traditional methods (e.g., line transects, sound playback) that are poorly efficient for cryptic primates, such as the black lion tamarin (Leontopithecus chrysopygus). Here we investigated the effectiveness of passive acoustic monitoring (PAM) as an alternative method to identify the presence of vocalizing lion tamarins in the wild. We aimed to: (1) determine the maximum distance at which autonomous recorders (Song Meter 3) and Raven Pro acoustic software can respectively detect and identify lion tamarin long calls emitted by two captive subjects (ex situ study); and (2) determine the sampling effort required to confirm the presence of the species in the wild (in situ study). In captive settings, we recorded lion tamarin long calls with one to two autonomous recorders operating at increasing distances from the animals' enclosure (8-202 m). In a 515 ha forest fragment, we deployed 12 recorders in a grid, 300 m apart from each other, within the estimated 100 ha home range of one group, and let them record for 10 consecutive days, totaling 985 h. In the ex situ study, hand-browsing of spectrograms yielded 298 long calls emitted from 8 to 194 m, and Raven's Template Detector identified 54.6% of them, also emitted from 8 to 194 m. In the in situ study, we manually counted 1115 long calls, and the Raven's Template Detector identified 44.75% of them. Furthermore, the presence of lion tamarins was confirmed within 1 day using four randomly sorted recorders, whereas 5 days on average were necessary with only one device. While specific protocols still need to be developed to determine primate population size using this technology, we concluded that PAM is a promising tool when considering long term costs and benefits.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

Large-scale simulation of bird localization systems in forests with distributed microphone arrays

Acoustic wildlife monitoring systems are important tools for capturing information about animal habitation in ecosystems. Previous work has demonstrated the effectiveness of audio-based bird localization techniques. However, few studies have investigated the performance and robustness of distributed systems in large forests. Here, the performance of distributed microphone arrays for localizing birds is examined by simulating forest scenes with added reverberation, ambient noise, and measurement errors. The simulation revealed the importance of the signal-to-noise ratio and the spectral weighting in the localization algorithm. These results may guide the design of large-scale wildlife monitoring systems and suggest promising directions for further improvements.

Species detection framework using automated recording units: a case study of the Critically Endangered Jerdon's courser

With the advent of automated recording units, bioacoustic monitoring has become a popular tool for the collection of long-term data across extensive landscapes. Such methods involve two main components: hardware for audio data acquisition and software for analysis. In the acoustic monitoring of threatened species, a species-specific framework is often essential. Jerdon's courser Rhinoptilus bitorquatus is a Critically Endangered nocturnal bird endemic to a small region of the Eastern Ghats of India, last reported in 2008. Here we describe a reproducible and scalable acoustic detection framework for the species, comparing several commonly available hardware and detection methods and using existing software. We tested this protocol by collecting 24,349 h of data during 5 months. We analysed the data with two commercially available sound analysis programmes, following an analysis pipeline created for this species. Although we did not detect vocalizations of Jerdon's courser, this study provides a framework using a combination of hardware and software for future research that other conservation practitioners can implement. Vocal mimicry can aid or confound in detection and we highlight the potential role of mimicry in the detection of such threatened species. This species-specific acoustic detection framework can be scaled and tailored to monitor other species.

Towards Automated Detection and Localization of Red Deer Cervus elaphus Using Passive Acoustic Sensors during the Rut

Passive acoustic sensors have the potential to become a valuable complementary component in red deer Cervus elaphus monitoring providing deeper insight into the behavior of stags during the rutting period. Automation of data acquisition and processing is crucial for adaptation and wider uptake of acoustic monitoring. Therefore, an automated data processing workflow concept for red deer call detection and localization was proposed and demonstrated. The unique dataset of red deer calls during the rut in September 2021 was collected with four GPS time-synchronized microphones. Five supervised machine learning algorithms were tested and compared for the detection of red deer rutting calls where the support-vector-machine-based approach demonstrated the best performance of −96.46% detection accuracy. For sound source location, a hyperbolic localization approach was applied. A novel approach based on cross-correlation and spectral feature similarity was proposed for sound delay assessment in multiple microphones resulting in the median localization error of 16 m, thus providing a solution for automated sound source localization—the main challenge in the automation of the data processing workflow. The automated approach outperformed manual sound delay assessment by a human expert where the median localization error was 43 m. Artificial sound records with a known location in the pilot territory were used for localization performance testing.

Acoustic approach as an alternative to human-based survey in bird biodiversity monitoring in agricultural meadows

Acoustic monitoring has been tested as an alternative to the traditional, human-based approach of surveying birds, however studies examining the effectiveness of different acoustic methods sometimes yield inconsistent results. In this study we examined whether bird biodiversity estimated by traditional surveys of birds differs to that obtained through soundscape surveys in meadow habitats that are of special agricultural importance, and whether acoustic monitoring can deliver reliable indicators of meadows and farmland bird biodiversity. We recorded soundscape and simultaneously surveyed birds by highly skilled human-observers within a fixed (50 m and 100 m) and unlimited radius using the point-count method twice in the breeding season at 74 recording sites located in meadows, in order to compare differences in (1) bird biodiversity estimation of meadow, farmland, songbird, and all bird species and (2) the detection rate of single bird species by these two methods. We found that recorders detected more species in comparison to the human-observers who surveyed birds within a fixed radius (50 and 100 m) and fewer when detection distance for human-observers was unlimited. We did not find significant differences in the number of meadow and farmland bird species detected by recorders and observers within a 100 m radius–the most often used fixed radius in traditional human based point-counts. We also showed how detection rate of 48 the most common bird species in our study differ between these two methods. Our study showed that an acoustic survey is equally effective as human observers surveying birds within a 100 m radius in estimation of farmland and meadow bird biodiversity. These groups of species are important for agricultural landscape and commonly used as indicators of habitat quality and its changes. Even though recorders rarely detect species that remain mostly silent during the observation periods, or species that are further distant than 100 m away, we recommend using acoustic soundscape recording methods as an equally effective and more easily standardised alternative for monitoring of farmland and meadow bird biodiversity. We propose adaptation of acoustic approach to long-term, large-scale monitoring by collecting acoustic data by non-specialists, including landowners and volunteers, and analysing them in a standardised way by units supervising monitoring of agriculture landscape.

Bird population declines and species turnover are changing the acoustic properties of spring soundscapes

Natural sounds, and bird song in particular, play a key role in building and maintaining our connection with nature, but widespread declines in bird populations mean that the acoustic properties of natural soundscapes may be changing. Using data-driven reconstructions of soundscapes in lieu of historical recordings, here we quantify changes in soundscape characteristics at more than 200,000 sites across North America and Europe. We integrate citizen science bird monitoring data with recordings of individual species to reveal a pervasive loss of acoustic diversity and intensity of soundscapes across both continents over the past 25 years, driven by changes in species richness and abundance. These results suggest that one of the fundamental pathways through which humans engage with nature is in chronic decline, with potentially widespread implications for human health and well-being.

Evaluation of Accuracy and Precision of the Sound-Recorder-Based Point-Counts Applied in Forests and Open Areas in Two Locations Situated in a Temperate and Tropical Regions

The point-count method is one of the most popular techniques for surveying birds. However, the accuracy and precision of this method may vary across various environments and geographical regions. We conducted sound-recorder-based point-counts to examine the accuracy and precision of the method for bird biodiversity estimation as a function of geographical region, habitat type and the time of day at which the survey began. In temperate (Poland) and tropical (Cameroon) regions, we recorded soundscapes on two successive mornings at 36 recording sites (18 in each location). At each site, we analyzed three 5-min surveys per day. We found no differences in the accuracy and precision of the method between regions and habitats. The accuracy was significantly greater at sunrise than during later surveys. The similarity of the bird assemblages detected by different surveys did not differ between regions or habitats. However, the bird communities described at the same time of day were significantly more similar to each other than those detected by surveys conducted at different times. The point-count method provided statistically indistinguishable estimates of bird biodiversity in different geographical regions and habitats. However, our results highlight two weaknesses of the method: low accuracy (41–54%), which limits the usefulness of a single survey in understanding bird–environment relationships, and changes in accuracy throughout the day, which may result in the misinterpretation of the status of bird populations.

Automated detection of frog calls and choruses by pulse repetition rate

Anurans (frogs and toads) are among the most globally threatened taxonomic groups. Successful conservation of anurans will rely on improved data on the status and changes in local populations, particularly for rare and threatened species. Automated sensors, such as acoustic recorders, have the potential to provide such data by massively increasing the spatial and temporal scale of population sampling efforts. Analyzing such data sets will require robust and efficient tools that can automatically identify the presence of a species in audio recordings. Like bats and birds, many anuran species produce distinct vocalizations that can be captured by autonomous acoustic recorders and represent excellent candidates for automated recognition. However, in contrast to birds and bats, effective automated acoustic recognition tools for anurans are not yet widely available. An effective automated call-recognition method for anurans must be robust to the challenges of real-world field data and should not require extensive labeled data sets. We devised a vocalization identification tool that classifies anuran vocalizations in audio recordings based on their periodic structure: the repeat interval-based bioacoustic identification tool (RIBBIT). We applied RIBBIT to field recordings to study the boreal chorus frog (Pseudacris maculata) of temperate North American grasslands and the critically endangered variable harlequin frog (Atelopus varius) of tropical Central American rainforests. The tool accurately identified boreal chorus frogs, even when they vocalized in heavily overlapping choruses and identified variable harlequin frog vocalizations at a field site where it had been very rarely encountered in visual surveys. Using a few simple parameters, RIBBIT can detect any vocalization with a periodic structure, including those of many anurans, insects, birds, and mammals. We provide open-source implementations of RIBBIT in Python and R to support its use for other taxa and communities.

Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture

The use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Here we test SincNet, an efficient neural network architecture that learns from the raw waveform using sinc-based filters. Results using an off-the-shelf implementation of SincNet on a publicly available bird sound dataset (NIPS4Bplus) show that the neural network rapidly converged reaching accuracies of over 65% with limited data. Their performance is comparable with traditional methods after hyperparameter tuning but they are more efficient. Learning directly from the raw waveform allows the algorithm to select automatically those elements of the sound that are best suited for the task, bypassing the onerous task of selecting feature extraction techniques and reducing possible biases. We use publicly released code and datasets to encourage others to replicate our results and to apply SincNet to their own datasets; and we review possible enhancements in the hope that algorithms that learn from the raw waveform will become useful bioacoustic tools.

Roadside Car Surveys: Methodological Constraints and Solutions for Estimating Parrot Abundances across the World

Parrots stand out among birds because of their poor conservation status and the lack of available information on their population sizes and trends. Estimating parrot abundance is complicated by the high mobility, gregariousness, patchy distributions, and rarity of many species. Roadside car surveys can be useful to cover large areas and increase the probability of detecting spatially aggregated species or those occurring at very low densities. However, such surveys may be biased due to their inability to handle differences in detectability among species and habitats. We conducted 98 roadside surveys, covering > 57,000 km across 20 countries and the main world biomes, recording ca. 120,000 parrots from 137 species. We found that larger and more gregarious species are more easily visually detected and at greater distances, with variations among biomes. However, raw estimates of relative parrot abundances (individuals/km) were strongly correlated (r = 0.86–0.93) with parrot densities (individuals/km2) estimated through distance sampling (DS) models, showing that variability in abundances among species (>40 orders of magnitude) overcomes any potential detectability bias. While both methods provide similar results, DS cannot be used to study parrot communities or monitor the population trends of all parrot species as it requires a minimum of encounters that are not reached for most species (64% in our case), mainly the rarest and more threatened. However, DS may be the most suitable choice for some species-specific studies of common species. We summarize the strengths and weaknesses of both methods to guide researchers in choosing the best–fitting option for their particular research hypotheses, characteristics of the species studied, and logistical constraints.

Combining point counts and autonomous recording units improves avian survey efficacy across elevational gradients on two continents

Accurate biodiversity and population monitoring is a requirement for effective conservation decision making. Survey method bias is therefore a concern, particularly when research programs face logistical and cost limitations.We employed point counts (PCs) and autonomous recording units (ARUs) to survey avian biodiversity within comparable, high elevation, temperate mountain habitats at opposite ends of the Americas: nine mountains in British Columbia (BC), Canada, and 10 in southern Chile. We compared detected species richness against multiyear species inventories and examined method-specific detection probability by family. By incorporating time costs, we assessed the performance and efficiency of single versus combined methods.Species accumulation curves indicate ARUs can capture ~93% of species present in BC but only ~58% in Chile, despite Chilean mountain communities being less diverse. The avian community, rather than landscape composition, appears to drive this dramatic difference. Chilean communities contain less-vocal species, which ARUs missed. Further, 6/13 families in BC were better detected by ARUs, while 11/11 families in Chile were better detected by PCs. Where survey conditions differentially impacted method performance, PCs mostly varied over the morning and with canopy cover in BC, while ARUs mostly varied seasonally in Chile. Within a single year of monitoring, neither method alone was predicted to capture the full avian community, with the exception of ARUs in the alpine and subalpine of BC. PCs contributed little to detected diversity in BC, but including this method resulted in negligible increases in total time costs. Combining PCs with ARUs in Chile significantly increased species detections, again, for little cost.Combined methods were among the most efficient and accurate approaches to capturing diversity. We recommend conducting point counts, while ARUs are being deployed and retrieved in order to capture additional diversity with minimal additional effort and to flag methodological biases using a comparative framework.

Not by the light of the moon: Investigating circadian rhythms and environmental predictors of calling in Bornean great argus

Great argus pheasants are known for their elaborate visual mating displays, but relatively little is known about their general ecology. The use of passive acoustic monitoring-which relies on long-term autonomous recorders-can provide insight into the behavior of visually cryptic, yet vocal species such as the great argus. Here we report the results of an analysis of vocal behavior of the Bornean great argus (Argusianus argus grayi) in Sabah, Malaysia, using data collected with 11 autonomous recording units. Great argus regularly emitted two call types, the long call and the short call, and we found that although both call types were emitted throughout the day, the short calls were more likely to occur during the morning hours (06:00-12:00LT). Great argus were less likely to call if there was rain, irrespective of the time of day. A substantial portion of calls at our site (~20%) were emitted between the hours of 18:00-06:00LT. We found that for nighttime calls, calling activity increased during new moon periods and decreased during periods of rain. We attribute the negative influence of rain on calling to increased energetic costs of thermoregulation during wet periods, and propose that the influence of the lunar cycle may be related to increased predation risk during periods with high levels of moonlight. Little is known about the behavioral ecology of great argus on Borneo, so it is difficult to know if the results we report are typical, or if we would see differences in calling activity patterns depending on breeding season or changes in food availability. We advocate for future studies of great argus pheasant populations using paired camera and acoustic recorders, which can provide further insight into the behavior of this cryptic species.

Comparing detectability patterns of bird species using multi-method occupancy modelling

A robust knowledge of biodiversity distribution is essential for designing and developing effective conservation actions. The choice of a suitable sampling method is key to obtaining sufficiently accurate information of species distribution and consequently to improve biodiversity conservation. This study applies multi-method occupancy models to 36 common bird species associated with small ponds in the province of Murcia (south-eastern Spain), one of the most arid regions of Europe, in order to compare their effectiveness for detecting different bird species: direct observation, combined observation and video monitoring and mist netting captures. The results showed that the combined method and direct observation were similar and most effective than mist netting for detecting species occupancy, although detection rates ranged widely among bird groups, while some large species were poorly detected by all the methods used. Average detectability did not increase during the breeding period. The chosen approach is particularly applicable to both single- and multi-species bird monitoring programmes. However, we recommend evaluating the cost-effectiveness of all the available methods in order to reduce costs and improve the success of sampling designs.

How much survey effort is required to assess bird assemblages in fire-prone eucalypt forests using acoustic recorders?

Abstract ContextForest fire activity is expected to increase in many parts of the globe over the course of the 21st century, with corresponding potential for heightened levels of proximate and ultimate threats to avian diversity. Landscape-scale investigations of the responses of birds in locations where current extreme fire regimes represent those expected in the future provide opportunities to identify potentially vulnerable species in advance. Autonomous acoustic recorders are well suited to survey birds in the typically large and remote natural areas with low accessibility required for these types of studies, because they offer cost-effective and relatively safe options for obtaining reliable data. AimsThe present study aimed to optimise survey using acoustic recorders to achieve a satisfactory assessment of montane dry sclerophyll forest bird assemblages using these devices. Survey completeness, or the number of species detected as a percentage of total species, was used as a metric to gauge survey suitability. MethodsAcoustic recorders were deployed in 10 ridge-top forest sites in the Blue Mountains, south-eastern Australia. Extensive field recordings were processed by an analyst, with species detected by their calls recorded in a series of 20-min samples. A results-based approach, incorporating a stopping rule that established when to conclude sampling at a site, was applied to the data. The results guided the target survey completeness and sampling effort levels assigned to a set of fixed-effort survey methods, which were subsequently evaluated. Key resultsThe optimal survey method involved using recordings from five 20-min sampling periods immediately following dawn for 2 days, achieving an average survey completeness level of 69%. ConclusionsThe optimal survey method can obtain results that are suitable for many types of studies involving assessments of bird assemblages, because the method can detect all common and moderately common species in assemblages, plus a fair proportion of rare species. ImplicationsThe present study has systematically developed an effective method of using autonomous acoustic recorders to research and monitor montane bird assemblages in fire-prone dry sclerophyll forests. This methodological approach may also be applied in systems subject to altered patterns of flood, storm or other extreme weather under climate change.

BioSounds: an open-source, online platform for ecoacoustics

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings. However, the management of these raw data presents technical challenges and their analysis suffers from bottlenecks. A multitude of software solutions exist, but none can perform all the data processing needed by ecologists for analysing large acoustic data sets. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present BioSounds: an open-source, online platform for ecoacoustics designed by ecologists and built by software engineers. Biosounds can be used for archiving and sharing recordings, manually creating and reviewing annotations of sonant animals in soundscapes, analysing audio in time and frequency, and storing reference recordings for different taxa. We present its features and structure, and compare it with similar software. We describe its operation mode and the workflow for typical use cases such as the analysis of bird and bat communities sampled in soundscape recordings. BioSounds is available from: https://github.com/nperezg/biosounds.

ecoSound-web: an open-source, online platform for ecoacoustics

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings, but the management and analyses of these raw data present technical challenges. A multitude of software solutions exist, but none can fulfil all purposes required for the management, processing, navigation, analysis, and dissemination of acoustic data. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present ecoSound-web: an open-source, online platform for ecoacoustics designed and built by ecologists and software engineers. ecoSound-web can be used for storing, organising, and sharing soundscape projects, manually creating and peer-reviewing annotations of soniferous animals and phonies, analysing audio in time and frequency, computing alpha acoustic indices, and providing reference sound libraries for different taxa. We present ecoSound-web's features, structure, and compare it with similar software. We describe its operation mode and the workflow for typical use cases such as the sampling of bird and bat communities, the use of a primate call library, and the analysis of phonies and acoustic indices. ecoSound-web is available from: https://github.com/ecomontec/ecoSound-web.

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Autonomous acoustic recorders are an increasingly popular method for low-disturbance, large-scale monitoring of sound-producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time-synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time-synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor-intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open-source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low-disturbance, large-scale collection of animal position data.

Assembling cheap, high-performance microphones for recording terrestrial wildlife: the Sonitor system

Passive acoustic monitoring of wildlife requires sound recording systems. Several cheap, high-performance, or open-source solutions currently exist for recording soundscapes, but all rely on commercial microphones. Commercial microphones are relatively expensive, specialized for particular taxa, and often have incomplete technical specifications. We designed Sonitor, an open-source microphone system to address all needs of ecologists that sample terrestrial wildlife acoustically. We evaluated the cost and durability of our system and measured trade-offs that are seldom acknowledged but which universally limit microphones' functions: weatherproofing versus sound attenuation, windproofing versus transmission loss after rain, signal loss in long cables, and analog sound amplification versus directivity with acoustic horns. We propose five microphone configurations suiting different budgets (from 8 to 33 EUR per unit), and fulfilling different sound quality and flexibility requirements. The Sonitor system consists of sturdy acoustic sensors that cover the entire sound frequency spectrum of sonant terrestrial wildlife at a fraction of the cost of commercial microphones.